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NEW QUESTION # 20
With exceptions for transformations or timeshifts, at what resolution do detectors operate?
- A. The resolution of the chart
- B. Native resolution
- C. The resolution of the dashboard
- D. 10 seconds
Answer: B
Explanation:
Explanation
According to the Splunk Observability Cloud documentation1, detectors operate at the native resolution of the metric or dimension that they monitor, with some exceptions for transformations or timeshifts. The native resolution is the frequency at which the data points are reported by the source. For example, if a metric is reported every 10 seconds, the detector will evaluate the metric every 10 seconds. The native resolution ensures that the detector uses the most granular and accurate data available for alerting.
NEW QUESTION # 21
Which component of the OpenTelemetry Collector allows for the modification of metadata?
- A. Receivers
- B. Processors
- C. Pipelines
- D. Exporters
Answer: B
Explanation:
Explanation
The component of the OpenTelemetry Collector that allows for the modification of metadata is A. Processors.
Processors are components that can modify the telemetry data before sending it to exporters or other components. Processors can perform various transformations on metrics, traces, and logs, such as filtering, adding, deleting, or updating attributes, labels, or resources. Processors can also enrich the telemetry data with additional metadata from various sources, such as Kubernetes, environment variables, or system information1 For example, one of the processors that can modify metadata is the attributes processor. This processor can update, insert, delete, or replace existing attributes on metrics or traces. Attributes are key-value pairs that provide additional information about the telemetry data, such as the service name, the host name, or the span kind2 Another example is the resource processor. This processor can modify resource attributes on metrics or traces.
Resource attributes are key-value pairs that describe the entity that produced the telemetry data, such as the cloud provider, the region, or the instance type3 To learn more about how to use processors in the OpenTelemetry Collector, you can refer to this documentation1.
1: https://opentelemetry.io/docs/collector/configuration/#processors 2:
https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/attributesprocessor 3:
https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/resourceprocessor
NEW QUESTION # 22
An SRE creates an event feed chart in a dashboard that shows a list of events that meet criteria they specify. Which of the following should they include? (select all that apply)
- A. Random alerts from active detectors.
- B. Events created when a detector triggers an alert.
- C. Custom events that have been sent in from an external source.
- D. Events created when a detector clears an alert.
Answer: B,C,D
Explanation:
According to the web search results1, an event feed chart is a type of chart that shows a list of events that meet criteria you specify. An event feed chart can display one or more event types depending on how you specify the criteria. The event types that you can include in an event feed chart are:
Custom events that have been sent in from an external source: These are events that you have created or received from a third-party service or tool, such as AWS CloudWatch, GitHub, Jenkins, or PagerDuty. You can send custom events to Splunk Observability Cloud using the API or the Event Ingest Service.
Events created when a detector triggers or clears an alert: These are events that are automatically generated by Splunk Observability Cloud when a detector evaluates a metric or dimension and finds that it meets the alert condition or returns to normal. You can create detectors to monitor and alert on various metrics and dimensions using the UI or the API.
Therefore, option A, B, and D are correct.
NEW QUESTION # 23
What constitutes a single metrics time series (MTS)?
- A. A set of data points that all have the same metric name and list of dimensions.
- B. A set of metrics that are ordered in series based on timestamp.
- C. A set of data points that use different dimensions but the same metric name.
- D. A series of timestamps that all reflect the same metric.
Answer: A
Explanation:
The correct answer is B. A set of data points that all have the same metric name and list of dimensions.
A metric time series (MTS) is a collection of data points that have the same metric and the same set of dimensions. For example, the following sets of data points are in three separate MTS:
MTS1: Gauge metric cpu.utilization, dimension "hostname": "host1" MTS2: Gauge metric cpu.utilization, dimension "hostname": "host2" MTS3: Gauge metric memory.usage, dimension "hostname": "host1" A metric is a numerical measurement that varies over time, such as CPU utilization or memory usage. A dimension is a key-value pair that provides additional information about the metric, such as the hostname or the location. A data point is a combination of a metric, a dimension, a value, and a timestamp1
NEW QUESTION # 24
Which of the following are ways to reduce flapping of a detector? (select all that apply)
- A. Apply a smoothing transformation (like a rolling mean) to the input data for the detector.
- B. Establish a reset threshold for the detector.
- C. Configure a duration or percent of duration for the alert.
- D. Enable the anti-flap setting in the detector options menu.
Answer: A,C
Explanation:
Explanation
According to the Splunk Lantern article Resolving flapping detectors in Splunk Infrastructure Monitoring, flapping is a phenomenon where alerts fire and clear repeatedly in a short period of time, due to the signal fluctuating around the threshold value. To reduce flapping, the article suggests the following ways:
Configure a duration or percent of duration for the alert: This means that you require the signal to stay above or below the threshold for a certain amount of time or percentage of time before triggering an alert. This can help filter out noise and focus on more persistent issues.
Apply a smoothing transformation (like a rolling mean) to the input data for the detector: This means that you replace the original signal with the average of its last several values, where you can specify the window length. This can reduce the impact of a single extreme observation and make the signal less fluctuating.
NEW QUESTION # 25
Which of the following statements about adding properties to MTS are true? (select all that apply)
- A. Properties can be set via the API.
- B. Properties are applied to dimension key:value pairs and propagated to all MTS with that dimension
- C. Properties can be set in the UI under Metric Metadata.
- D. Properties are sent in with datapoints.
Answer: A,C
Explanation:
According to the web search results, properties are key-value pairs that you can assign to dimensions of existing metric time series (MTS) in Splunk Observability Cloud1. Properties provide additional context and information about the metrics, such as the environment, role, or owner of the dimension. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host that is sending the data is used for QA.
To add properties to MTS, you can use either the API or the UI. The API allows you to programmatically create, update, delete, and list properties for dimensions using HTTP requests2. The UI allows you to interactively create, edit, and delete properties for dimensions using the Metric Metadata page under Settings3. Therefore, option A and D are correct.
NEW QUESTION # 26
Which of the following are true about organization metrics? (select all that apply)
- A. A user can plot and alert on them like metrics they send to Splunk Observability Cloud.
- B. Organization metrics are included for free.
- C. Organization metrics count towards custom MTS limits.
- D. Organization metrics give insights into system usage, system limits, data ingested and token quotas.
Answer: A,B,D
Explanation:
Explanation
The correct answer is A, C, and D. Organization metrics give insights into system usage, system limits, data ingested and token quotas. Organization metrics are included for free. A user can plot and alert on them like metrics they send to Splunk Observability Cloud.
Organization metrics are a set of metrics that Splunk Observability Cloud provides to help you measure your organization's usage of the platform. They include metrics such as:
Ingest metrics: Measure the data you're sending to Infrastructure Monitoring, such as the number of data points you've sent.
App usage metrics: Measure your use of application features, such as the number of dashboards in your organization.
Integration metrics: Measure your use of cloud services integrated with your organization, such as the number of calls to the AWS CloudWatch API.
Resource metrics: Measure your use of resources that you can specify limits for, such as the number of custom metric time series (MTS) you've created1 Organization metrics are not charged and do not count against any system limits. You can view them in built-in charts on the Organization Overview page or in custom charts using the Metric Finder. You can also create alerts based on organization metrics to monitor your usage and performance1 To learn more about how to use organization metrics in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/observability/admin/org-metrics.html
NEW QUESTION # 27
Clicking a metric name from the results in metric finder displays the metric in Chart Builder. What action needs to be taken in order to save the chart created in the UI?
- A. Make sure that data is coming in for the metric then save the chart.
- B. Save the chart to multiple dashboards.
- C. Save the chart to a dashboard.
- D. Create a new dashboard and save the chart.
Answer: C
Explanation:
According to the web search results, clicking a metric name from the results in metric finder displays the metric in Chart Builder1. Chart Builder is a tool that allows you to create and customize charts using metrics, dimensions, and analytics functions2. To save the chart created in the UI, you need to do the following steps:
Click the Save button on the top right corner of the Chart Builder. This will open a dialog box where you can enter the chart name and description, and choose the dashboard where you want to save the chart.
Enter a name and a description for your chart. The name should be descriptive and unique, and the description should explain the purpose and meaning of the chart.
Choose an existing dashboard from the drop-down menu, or create a new dashboard by clicking the + icon. A dashboard is a collection of charts that display metrics and events for your services or hosts3. You can organize and share dashboards with other users in your organization using dashboard groups3.
Click Save. This will save your chart to the selected dashboard and redirect you to the dashboard view. You can also access your saved chart from the Dashboards menu on the left navigation bar.
NEW QUESTION # 28
When creating a standalone detector, individual rules in it are labeled according to severity. Which of the choices below represents the possible severity levels that can be selected?
- A. Info, Warning, Minor, Major, and Critical.
- B. Info, Warning, Minor, Major, and Emergency.
- C. Info, Warning, Minor, Severe, and Critical.
- D. Debug, Warning, Minor, Major, and Critical.
Answer: A
Explanation:
The correct answer is C. Info, Warning, Minor, Major, and Critical.
When creating a standalone detector, you can define one or more rules that specify the alert conditions and the severity level for each rule. The severity level indicates how urgent or important the alert is, and it can also affect the notification settings and the escalation policy for the alert1 Splunk Observability Cloud provides five predefined severity levels that you can choose from when creating a rule: Info, Warning, Minor, Major, and Critical. Each severity level has a different color and icon to help you identify the alert status at a glance. You can also customize the severity levels by changing their names, colors, or icons2 To learn more about how to create standalone detectors and use severity levels in Splunk Observability Cloud, you can refer to these documentations12.
1: https://docs.splunk.com/Observability/alerts-detectors-notifications/detectors.html#Create-a-standalone-detector 2: https://docs.splunk.com/Observability/alerts-detectors-notifications/detector-options.html#Severity-levels
NEW QUESTION # 29
An SRE came across an existing detector that is a good starting point for a detector they want to create. They clone the detector, update the metric, and add multiple new signals. As a result of the cloned detector, which of the following is true?
- A. The new signals will be reflected in the original detector.
- B. The new signals will be reflected in the original chart.
- C. The new signals will not be added to the original detector.
- D. You can only monitor one of the new signals.
Answer: C
Explanation:
According to the Splunk O11y Cloud Certified Metrics User Track document1, cloning a detector creates a copy of the detector that you can modify without affecting the original detector. You can change the metric, filter, and signal settings of the cloned detector. However, the new signals that you add to the cloned detector will not be reflected in the original detector, nor in the original chart that the detector was based on. Therefore, option D is correct.
Option A is incorrect because the new signals will not be reflected in the original detector. Option B is incorrect because the new signals will not be reflected in the original chart. Option C is incorrect because you can monitor all of the new signals that you add to the cloned detector.
NEW QUESTION # 30
A customer is experiencing issues getting metrics from a new receiver they have configured in the OpenTelemetry Collector. How would the customer go about troubleshooting further with the logging exporter?
- A. Adding logging into the metrics receiver pipeline:
- B. Adding logging into the metrics exporter pipeline:
- C. Adding debug into the metrics receiver pipeline:
- D. Adding debug into the metrics exporter pipeline:
Answer: A
Explanation:
The correct answer is B. Adding logging into the metrics receiver pipeline.
The logging exporter is a component that allows the OpenTelemetry Collector to send traces, metrics, and logs directly to the console. It can be used to diagnose and troubleshoot issues with telemetry received and processed by the Collector, or to obtain samples for other purposes1 To activate the logging exporter, you need to add it to the pipeline that you want to diagnose. In this case, since you are experiencing issues with a new receiver for metrics, you need to add the logging exporter to the metrics receiver pipeline. This will create a new plot that shows the metrics received by the Collector and any errors or warnings that might occur1 The image that you have sent with your question shows how to add the logging exporter to the metrics receiver pipeline. You can see that the exporters section of the metrics pipeline includes logging as one of the options. This means that the metrics received by any of the receivers listed in the receivers section will be sent to the logging exporter as well as to any other exporters listed2 To learn more about how to use the logging exporter in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/Observability/gdi/opentelemetry/components/logging-exporter.html 2: https://docs.splunk.com/Observability/gdi/opentelemetry/exposed-endpoints.html
NEW QUESTION # 31
Which of the following are supported rollup functions in Splunk Observability Cloud?
- A. 1min, 5min, 10min, 15min, 30min
- B. sigma, epsilon, pi, omega, beta, tau
- C. average, latest, lag, min, max, sum, rate
- D. std_dev, mean, median, mode, min, max
Answer: C
Explanation:
Explanation
According to the Splunk O11y Cloud Certified Metrics User Track document1, Observability Cloud has the following rollup functions: Sum: (default for counter metrics): Returns the sum of all data points in the MTS reporting interval. Average (default for gauge metrics): Returns the average value of all data points in the MTS reporting interval. Min: Returns the minimum data point value seen in the MTS reporting interval. Max:
Returns the maximum data point value seen in the MTS reporting interval. Latest: Returns the most recent data point value seen in the MTS reporting interval. Lag: Returns the difference between the most recent and the previous data point values seen in the MTS reporting interval. Rate: Returns the rate of change of data points in the MTS reporting interval. Therefore, option A is correct.
NEW QUESTION # 32
When creating a standalone detector, individual rules in it are labeled according to severity. Which of the choices below represents the possible severity levels that can be selected?
- A. Info, Warning, Minor, Major, and Critical.
- B. Info, Warning, Minor, Major, and Emergency.
- C. Info, Warning, Minor, Severe, and Critical.
- D. Debug, Warning, Minor, Major, and Critical.
Answer: A
Explanation:
Explanation
The correct answer is C. Info, Warning, Minor, Major, and Critical.
When creating a standalone detector, you can define one or more rules that specify the alert conditions and the severity level for each rule. The severity level indicates how urgent or important the alert is, and it can also affect the notification settings and the escalation policy for the alert1 Splunk Observability Cloud provides five predefined severity levels that you can choose from when creating a rule: Info, Warning, Minor, Major, and Critical. Each severity level has a different color and icon to help you identify the alert status at a glance. You can also customize the severity levels by changing their names, colors, or icons2 To learn more about how to create standalone detectors and use severity levels in Splunk Observability Cloud, you can refer to these documentations12.
1:
https://docs.splunk.com/Observability/alerts-detectors-notifications/detectors.html#Create-a-standalone-detector
2: https://docs.splunk.com/Observability/alerts-detectors-notifications/detector-options.html#Severity-levels
NEW QUESTION # 33
What are the best practices for creating detectors? (select all that apply)
- A. Have a consistent type of measurement.
- B. View detector in a chart.
- C. Have a consistent value.
- D. View data at highest resolution.
Answer: A,B,C,D
Explanation:
Explanation
The best practices for creating detectors are:
View data at highest resolution. This helps to avoid missing important signals or patterns in the data that could indicate anomalies or issues1 Have a consistent value. This means that the metric or dimension used for detection should have a clear and stable meaning across different sources, contexts, and time periods. For example, avoid using metrics that are affected by changes in configuration, sampling, or aggregation2 View detector in a chart. This helps to visualize the data and the detector logic, as well as to identify any false positives or negatives. It also allows to adjust the detector parameters and thresholds based on the data distribution and behavior3 Have a consistent type of measurement. This means that the metric or dimension used for detection should have the same unit and scale across different sources, contexts, and time periods. For example, avoid mixing bytes and bits, or seconds and milliseconds.
1: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors 2:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors 3:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#View-detector-in-a-chart :
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors
NEW QUESTION # 34
A DevOps engineer wants to determine if the latency their application experiences is growing fester after a new software release a week ago. They have already created two plot lines, A and B, that represent the current latency and the latency a week ago, respectively. How can the engineer use these two plot lines to determine the rate of change in latency?
- A. Create a temporary plot by dragging items A and B into the Analytics Explorer window.
- B. Create a temporary plot by clicking the Change% button in the upper-right corner of the plot showing lines A and B.
- C. Create a plot C using the formula (A-B) and add a scale:percent function to express the rate of change as a percentage.
- D. Create a plot C using the formula (A/B-l) and add a scale: 100 function to express the rate of change as a percentage.
Answer: D
Explanation:
The correct answer is C. Create a plot C using the formula (A/B-l) and add a scale: 100 function to express the rate of change as a percentage.
To calculate the rate of change in latency, you need to compare the current latency (plot A) with the latency a week ago (plot B). One way to do this is to use the formula (A/B-l), which gives you the ratio of the current latency to the previous latency minus one. This ratio represents how much the current latency has increased or decreased relative to the previous latency. For example, if the current latency is 200 ms and the previous latency is 100 ms, then the ratio is (200/100-l) = 1, which means the current latency is 100% higher than the previous latency1 To express the rate of change as a percentage, you need to multiply the ratio by 100. You can do this by adding a scale: 100 function to the formula. This function scales the values of the plot by a factor of 100. For example, if the ratio is 1, then the scaled value is 100%2 To create a plot C using the formula (A/B-l) and add a scale: 100 function, you need to follow these steps:
Select plot A and plot B from the Metric Finder.
Click on Add Analytics and choose Formula from the list of functions.
In the Formula window, enter (A/B-l) as the formula and click Apply.
Click on Add Analytics again and choose Scale from the list of functions.
In the Scale window, enter 100 as the factor and click Apply.
You should see a new plot C that shows the rate of change in latency as a percentage.
To learn more about how to use formulas and scale functions in Splunk Observability Cloud, you can refer to these documentations34.
1: https://www.mathsisfun.com/numbers/percentage-change.html 2: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Scale 3: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Formula 4: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Scale
NEW QUESTION # 35
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